Interactive graphical system for estimating body measurements

ABSTRACT

Utilizing graphical elements representing human bodies to estimate physical measurements of a user is described. In at least one example, a service provider can access a database storing a plurality of data items. The service provider can cause a set of data items of the plurality of data items to be presented to the user. Data items in the set of data items are associated with at least one graphical element representing a human body with individual magnitudes corresponding to individual dimensions of a plurality of dimensions. The service provider can receive data indicating a selection of a data item associated with a first magnitude associated with a first dimension and a second magnitude associated with a second dimension. The service provider can estimate physical measurements associated with the user based partly on a first magnitude and/or the second magnitude.

RELATED APPLICATIONS

This application is a claims the benefit of U.S. Provisional ApplicationNo. 62/173,120 filed on Jun. 9, 2015, the entire contents of which areincorporated herein by reference.

BACKGROUND

Physical measurements of human bodies are useful for various purposes.For instance, health professionals use physical measurements tocalculate body mass index (BMI). BMI is a measurement of a person's bodyfat based on the person's height and weight that can be used todetermine whether the person is underweight, overweight, obese, etc.BMI, waist measurement, etc. can be useful for determining whetherpersons are at risk for various diseases. Additionally, many apparelcompanies use physical measurements to determine sizes and fits ofgarments. Collecting physical measurements, however, is time consumingAdditionally, persons can find themselves in situations where tools usedfor determining physical measurements are not accessible, such as in thedeveloping world, in online applications, etc.

SUMMARY

This disclosure describes utilizing graphical elements representinghuman bodies to estimate physical measurements of a person. In at leastone example, a service provider can access a database storing aplurality of data items. The service provider can cause a set of dataitems of the plurality of data items to be presented to the user. Dataitems in the set of data items are associated with at least onegraphical element representing a human body with individual magnitudescorresponding to individual dimensions of a plurality of dimensions. Theservice provider can receive data indicating a selection of a data itemassociated with a first magnitude associated with a first dimension anda second magnitude associated with a second dimension. The serviceprovider can estimate physical measurements associated with the userbased partly on a first magnitude and/or the second magnitude.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key or essentialfeatures of the claimed subject matter, nor is it intended to be used tolimit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The Detailed Description is set forth with reference to the accompanyingfigures, in which the left-most digit of a reference number identifiesthe figure in which the reference number first appears. The use of thesame reference numbers in the same or different figures indicatessimilar or identical items or features.

FIG. 1 is a schematic diagram showing an example environment forutilizing data items associated with graphical elements representinghuman bodies to estimate physical measurements.

FIG. 2 is a flow diagram that illustrates an example process to estimatephysical measurements utilizing data items associated with graphicalelements representing human bodies that are presented to users.

FIG. 3 is a flow diagram that illustrates another example process toestimate physical measurements utilizing data items associated withgraphical elements representing human bodies that are presented tousers.

FIG. 4 is a flow diagram that illustrates an example process to train apredictive model for estimating physical measurements.

DETAILED DESCRIPTION

This disclosure describes utilizing graphical elements representinghuman bodies to estimate physical measurements of a person. Technologiesdescribed herein can enable physical measurements to be estimatedwithout requiring access to measuring devices such as scales, measuringtapes, body fat meters, body fat calipers, etc. That is, thetechnologies described herein include estimating physical measurementswhen few or no measuring devices are available using data itemsassociated with graphical elements representing human bodies and inputsassociated with the data items. The technologies described herein can beused to replace, assist, and/or supplement technologies currentlyimplemented to determine physical measurements using various measuringdevices (e.g., scales, tape measures, etc.).

For illustrative purposes, a physical measurement represents a definitemagnitude of a physical quantity that is used as a standard forquantifying a dimension of a part of the human body and/or acharacteristic of the human body. Physical measurements can beassociated with any system of units (e.g., metric system, United Statescustomary measurement system, natural unit system, etc.). A physicalmeasurement can be a definite magnitude of a physical quantity ofdimension of a user's neck (e.g., circumference, length, width, etc.),upper arm (e.g., circumference, length, width, etc.), chest (e.g.,circumference, length, width, etc.), bust (e.g. circumference, etc.),waist (e.g., circumference, length, width, etc.), hips (e.g.,circumference, length, width, etc.), thigh (e.g., circumference, length,width, etc.), calf (e.g., circumference, length, width, etc.), leg(e.g., circumference, length, width, etc.), arm (e.g., circumference,length, width, etc.), etc. Physical measurements can includemeasurements of a user's height, weight, body fat percentage, etc.

Physical measurements can also include measurements that are determinedbased on other physical measurements, such as BMI, as described above.BMI is an estimate of a user's body fat based on the user's height andweight that can be used to determine whether a user is underweight,overweight, obese, etc. BMI can be useful for determining whether usersare at risk for various diseases. BMI is determined based on dividing auser's mass in kilograms by the square of the user's height in meters,or by multiplying a user's mass in pounds by 703 and dividing theproduct by the square of the user's height in inches.

Technologies described herein cause one or more sets of data items to bepresented to a user via an interface of a device that can be associatedwith the user (e.g. a user device). Each data item can be associatedwith at least one graphical element representing a human body havingindividual magnitudes of individual physical measurements correspondingto individual dimensions. That is, a data item can be associated with atleast one graphical element that graphically represents a human bodythat has proportions that are consistent with a real human body havingindividual physical measurements corresponding to individual magnitudes.The graphical elements can be graphical representations of human bodies,such as two-dimensional or three-dimensional graphical representationsof human bodies.

As described above, the technologies described herein can cause one ormore sets of data items to be presented to a user via an interface of adevice that can be associated with the user (e.g. a user device). A setof data items includes one or more data items. In at least one example,a set of data items can be a subset of data items from a database ofdata items. Individual data items in a set of data items can beassociated with at least one graphical element representing a human bodywith magnitudes associated with physical measurements that are differentfrom other individual data items in the set of data items. For instance,each of the data items in a set of data items can be associated with atleast one graphical element representing a human body with magnitudesincluding a first magnitude associated with a first dimension and asecond magnitude associated with a second dimension. In at least oneexample, the first magnitude can be the same for all data items in theset of data items and the second magnitude can be different for eachdata item in the set of data items. For instance, a set of data itemscan include data items associated with at least one graphical elementthat represents human bodies that have same BMIs and different waistmeasurements. In some examples, the magnitudes represented by graphicalrepresentations in each data item can differ from other data items in aset of data items by a single magnitude associated with a singledimension. That is, all of the magnitudes associated with all of thedimensions can be the same except for one magnitude associated with onedimension. For instance, a set of data items can include data items eachassociated with at least one graphical element that represents a humanbody that has all of the same magnitudes as the other data items in theset of data items except that each data item in the set of data items isassociated with at least one graphical element that represents a humanbody with a different waist measurement.

The user can select a data item that is associated with graphicalelements representing human bodies that look most like themselves, or insome examples, another person (e.g., a friend, a family member, asuspected criminal/person of interest, etc.), as described below. Theservice provider can receive data indicating the user selection. Basedat least in part on receiving the data associated with the userselection, the service provider can retrieve second (or subsequent) setsof data items. In at least one example, at least one of the magnitudesrepresented by individual data items in the second (or any subsequent)set of data items can be based on the magnitudes represented by the dataitem selected by the user from the previous set of data items. As anon-limiting example, if the second set of the data items includesindividual data items that are associated with one or more graphicalelements representing human bodies that have different BMIs, theindividual data items can be associated with one or more graphicalelements representing human bodies with a same waist measurement basedon the waist measurement corresponding to the data item selected by theuser in the first set of data items. The technologies described hereinleverage the user selection (e.g., input) and other user data toestimate physical measurements of the user (or the other person). Thatis, the technologies described herein can estimate physical measurementssuch as waist measurement, BMI, etc., without needing access tomeasuring devices.

Estimating physical measurements can be useful in various applicationsincluding, but not limited to, health and/or disease preventionapplications, video gaming applications, online apparel shoppingapplications, law enforcement applications, etc. For instance, healthand/or fitness products (e.g., Microsoft Band, Fitbit®, etc.) canutilize the technologies described herein to prompt users to select dataitems associated with graphical elements representing human bodies thatthey believe look most like themselves and to leverage the users'selections overtime to estimate how the users' bodies are changingand/or changes in physical measurements. This data can be correlatedwith other data collected from a health and/or fitness application(e.g., activity data, heart rate data, etc.) to enable users to trackprogress and enable users to see what activities, etc. impact theirhealth and/or fitness. In other examples, the technologies describedherein can be useful for disease identification and prevention. Asdescribed above, health professionals utilize physical measurements topredict whether a user is underweight, overweight, obese, etc., whetherusers are at risk for various diseases (e.g., cardiac, hypertension,diabetes, etc.), etc. Accordingly, the technologies described herein canbe utilized to identify and prevent diseases without relying onmeasuring devices.

In additional or alternative examples, the technologies described hereincan be utilized by gaming products and/or applications to generaterealistic depictions of how users appear (i.e., avatars). For instance,the technologies described herein can prompt a user to select data itemsassociated with graphical elements representing human bodies that theybelieve look most like themselves and can leverage the user's selectionsto generate realistic looking avatars. In other examples, thetechnologies described herein can be useful in online shoppingapplications. For instance, a user may not know his or her physicalmeasurements. Accordingly, the technologies described herein can prompta user to select data items associated with graphical elementsrepresenting human bodies that they believe look most like themselvesand can leverage the user's selections to estimate physical measurementsfor recommending sizes of apparel. Additionally or alternatively, a usercan be shopping online for a gift for another user (e.g., family member,friend, etc.) and he or she may not know the other user's physicalmeasurements and/or size. Accordingly, the technologies described hereincan prompt a user to select data items associated with graphicalelements representing human bodies that they believe look most like theother user and can leverage the user's selections to estimate physicalmeasurements for recommending sizes of apparel.

In yet another example, the technologies described herein can be usefulfor identifying suspected criminals/persons of interest and cantherefore be useful for law enforcement applications. For instance, thetechnologies described herein can prompt a user to select data itemsassociated with graphical elements representing human bodies that theybelieve look most like a suspect and can leverage the user's selectionsto estimate physical measurements for the suspect. The technologiesdescribed herein can also be used for searching through databases ofdata items associated with one or more graphical elements of knowncriminals.

Illustrative Environments

FIG. 1 is a schematic diagram showing an example environment 100 forutilizing data items associated with one or more graphical elementsrepresenting human bodies to estimate physical measurements. Moreparticularly, the example environment 100 can include a service provider102, one or more network(s) 104, one or more users 106, and one or moredevices 108 associated with the one or more users 106.

The service provider 102 can be any entity, server(s), platform, etc.,that facilitates presenting one or more sets of data items eachassociated with one or more graphical elements representing human bodiesand leveraging inputs associated with individual data items to estimatephysical measurements associated with users 106 and/or other persons(e.g., friends, family members, suspected criminals/persons of interest,etc.), as described above. The service provider 102 can be implementedin a non-distributed computing environment or can be implemented in adistributed computing environment, possibly by running some modules ondevices 108 or other remotely located devices. As shown, the serviceprovider 102 can include one or more server(s) 110, which can includeone or more processing unit(s) 112 and computer-readable media 114, suchas memory. In various examples, the service provider 102 can access dataitems associated with one or more graphical elements representing humanbodies from a database, cause the data items to be presented to a user106 in one or more sets of data items, receive data associated with userselection of individual data items, and leverage the magnitudesassociated with the individual data items selected by the user 106 andother user data to estimate physical measurements associated with theuser 106 and/or another person, as described above. The technologiesdescribed herein enable the service provider 102 to search and retrievedata items that are stored in the database and estimate physicalmeasurements based on said data items more efficiently (i.e., faster)than if humans were involved, as described below.

In some examples, the network(s) 104 can be any type of network known inthe art, such as the Internet. Moreover, the devices 108 cancommunicatively couple to the network(s) 104 in any manner, such as by aglobal or local wired or wireless connection (e.g., local area network(LAN), intranet, etc.). The network(s) 104 can facilitate communicationbetween the server(s) 110 and the devices 108 associated with the users106.

In some examples, the users 106 can operate corresponding devices 108(e.g., user devices) to perform various functions associated with thedevices 108, which can include one or more processing unit(s),computer-readable storage media, and a display 130. Device(s) 108 canrepresent a diverse variety of device types and are not limited to anyparticular type of device. Examples of device(s) 108 can include but arenot limited to stationary computers, mobile computers, embeddedcomputers, or combinations thereof. Example stationary computers caninclude desktop computers, work stations, personal computers, thinclients, terminals, game consoles, personal video recorders (PVRs),set-top boxes, or the like. Example mobile computers can include laptopcomputers, tablet computers, wearable computers, implanted computingdevices, telecommunication devices, automotive computers, portablegaming devices, media players, cameras, or the like. Example embeddedcomputers can include network enabled televisions, integrated componentsfor inclusion in a computing device, appliances, microcontrollers,digital signal processors, or any other sort of processing device, orthe like.

Examples support scenarios where device(s) that can be included in theone or more server(s) 110 can include one or more computing devices thatoperate in a cluster or other clustered configuration to shareresources, balance load, increase performance, provide fail-over supportor redundancy, or for other purposes. Device(s) included in the one ormore server(s) 110 can represent, but are not limited to, desktopcomputers, server computers, web-server computers, personal computers,mobile computers, laptop computers, tablet computers, wearablecomputers, implanted computing devices, telecommunication devices,automotive computers, network enabled televisions, thin clients,terminals, game consoles, gaming devices, work stations, media players,digital video recorders (DVRs), set-top boxes, cameras, integratedcomponents for inclusion in a computing device, appliances, or any othersort of computing device.

Device(s) that can be included in the one or more server(s) 110 caninclude any type of computing device having one or more processingunit(s) 112 operably connected to computer-readable media 114 such asvia a bus, which in some instances can include one or more of a systembus, a data bus, an address bus, a PCI bus, a Mini-PCI bus, and anyvariety of local, peripheral, and/or independent buses. Executableinstructions stored on computer-readable media 114 can include, forexample, a data collection module 116, storing user data 118, a database120, storing data items 122 associated with one or more graphicalelements 123A, 123B, etc., representing human bodies, a measurementestimation module 124, a presentation module 126, a training module 128,and other modules, programs, or applications that are loadable andexecutable by processing units(s) 112.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic componentssuch as accelerators. For example, and without limitation, illustrativetypes of hardware logic components that can be used includeField-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip Systems (SOCs), Complex Programmable Logic Devices(CPLDs), etc. Device(s) that can be included in the one or moreserver(s) 110 can further include one or more input/output (I/O)interface(s) coupled to the bus to allow device(s) to communicate withother devices such as input peripheral devices (e.g., a keyboard, amouse, a pen, a game controller, a voice input device, a touch inputdevice, gestural input device, and the like) and/or output peripheraldevices (e.g., a display, a printer, audio speakers, a haptic output,and the like). Such network interface(s) can include one or more networkinterface controllers (NICs) or other types of transceiver devices tosend and receive communications over a network. For simplicity, somecomponents are omitted from the illustrated environment.

Processing unit(s) 112 can represent, for example, a CPU-type processingunit, a GPU-type processing unit, a Field-programmable Gate Array(FPGA), another class of Digital Signal Processor (DSP), or otherhardware logic components that can, in some instances, be driven by aCPU. For example, and without limitation, illustrative types of hardwarelogic components that can be used include Application-SpecificIntegrated Circuits (ASICs), Application-Specific Standard Products(ASSPs), System-on-a-chip systems (SOCs), Complex Programmable LogicDevices (CPLDs), etc. In various examples, the processing unit(s) 112can execute one or more modules and/or processes to cause the server(s)110 to perform a variety of functions, as set forth above and explainedin further detail in the following disclosure. Additionally, each of theprocessing unit(s) 112 can possess its own local memory, which also canstore program modules, program data, and/or one or more operatingsystems.

In at least one configuration, the computer-readable media 114 of theserver(s) 110 can include components that facilitate interaction betweenthe service provider 102 and the users 106. The components can representpieces of code executing on a computing device. For example, thecomputer-readable media 114 can include the data collection module 116,the database 120, the measurement estimation module 124, thepresentation module 126, the training module 128, etc. In at least someexamples, the modules (116, 120, 126, 128, etc.) can be implemented ascomputer-readable instructions, various data structures, and so forthvia at least one processing unit(s) 112 to configure a device to executeinstructions and to perform operations implementing generating responsetemplates that correspond to ontological elements. Functionality toperform these operations can be included in multiple devices or a singledevice.

The computer-readable media 114 can also include the database 120 forstoring data items 122 associated with one or more graphical elements123A, 123B, etc. representing human bodies, as described above. As anon-limiting example, FIG. 1 shows example data items 122 associatedwith one or more graphical elements 123A, 123B, etc. representing humanbodies having a set of magnitudes. That is, the data items 122 can beassociated with one or more graphical elements 123A, 123B, etc. thatgraphically represent human bodies that have proportions that areconsistent with a human body having a set of physical measurementswhereby each individual physical measurement corresponds to anindividual magnitude, as described above. Each graphical element 123A,123B, etc. can be associated with a different view of the human bodyhaving the set of magnitudes. As a non-limiting example, FIG. 1 shows adata item 122, where one of the graphical elements 123A is a frontalview of a human body having a set of magnitudes and the other graphicalelement 124B is a profile view of the human body having the set ofmagnitudes. The database of data items 122 can include different dataitems 122 based on different demographics (e.g., nationalities,ethnicities, genders, ages, etc.).

Depending on the exact configuration and type of the server(s) 110, thecomputer-readable media 114 can include computer storage media and/orcommunication media. Computer storage media can include volatile memory,nonvolatile memory, and/or other persistent and/or auxiliary computerstorage media, removable and non-removable computer storage mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules, orother data. Computer memory is an example of computer storage media.Thus, computer storage media includes tangible and/or physical forms ofmedia included in a device and/or hardware component that is part of adevice or external to a device, including but not limited torandom-access memory (RAM), static random-access memory (SRAM), dynamicrandom-access memory (DRAM), phase change memory (PRAM), read-onlymemory (ROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), flashmemory, compact disc read-only memory (CD-ROM), digital versatile disks(DVDs), optical cards or other optical storage media, miniature harddrives, memory cards, magnetic cassettes, magnetic tape, magnetic diskstorage, magnetic cards or other magnetic storage devices or media,solid-state memory devices, storage arrays, network attached storage,storage area networks, hosted computer storage or any other storagememory, storage device, and/or storage medium that can be used to storeand maintain information for access by a computing device.

In contrast, communication media can embody computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave, or other transmissionmechanism. The term “modulated data signal” means a signal that has oneor more of its characteristics set or changed in such a manner as toencode information in the signal. Such signals or carrier waves, etc.can be propagated on wired media such as a wired network or direct-wiredconnection, and/or wireless media such as acoustic, RF, infrared andother wireless media. As defined herein, computer storage media does notinclude communication media. That is, computer storage media does notinclude communications media consisting solely of a modulated datasignal, a carrier wave, or a propagated signal, per se.

The data collection module 116 can receive data associated with users106 (e.g., user data 118) from the users 106 and/or on behalf of theusers 106 and/or access data associated with users 106 via third partysources and systems (e.g., social networks, professional networks,etc.). In some examples, users 106 can input user data 118 when they setup a user account or profile for interacting with the service provider102, an application, a website, etc., in response to a prompt for userdata 118, etc. In at least one example, the presentation module 126 cancause one or more user interfaces to be presented to the user 106. Theone or more user interfaces can provide functionality for the user 106to input information. The user 106 can input personal informationincluding, but not limited to, gender, age, physical measurements, etc.,to the data collection module 116.

In other examples, the data collection module 116 can receive data fromdevices such as input peripheral devices (e.g., sensors, cameras, andthe like). For instance, a camera and/or sensor can determine a user's106 height and provide data indicating the user's 106 height to the datacollection module 116. In additional or alternative examples, the datacollection module 116 can infer user data 118 based on user interactionswith the service provider 102. For instance, a user 106 can select adata item 122 representative of a female out of a set of data items 122representing both genders. Accordingly, the data collection module 116can infer that the user 106 is a female. In some examples, the datacollection module 116 can access user data 118 from third party sourcesand systems (e.g., social networks, professional networks, etc.). Theuser data 118 can be mapped to and/or otherwise associated with profilesthat correspond to individual users 106. The profiles that correspond tothe individual users 106 can be stored in a database associated with theuser data 118 and/or some other data repository.

The data collection module 116 can also receive data associated withuser selections of data items 122 from sets of data items that arepresented to the user 106. In at least one example, the presentationmodule 126 can cause sets of data items to be presented to the user 106via one or more personalized user interfaces, described below, thatprovide functionality for the user 106 to select individual data items122 of the set of data items 122. The user 106 can interact with thedata items 122 that are presented via the user interface utilizingvarious mechanisms. In some examples, the user 106 can interact with thedata items 122 via input peripheral devices (e.g., a keyboard, a mouse,a pen, a game controller, a voice input device, a touch input device,gestural input device, cameras, sensors, and the like), touch input,etc. The data collection module 116 can analyze the data to determinethe magnitudes that correspond to each of the data items 122 selected bythe user 106. The data collection module 116 can log each input andassociate the logs with a profile corresponding to the user 106 in thedatabase associated with the user data 118 and/or other data repository.Each log can correspond to a set of magnitudes that are represented bythe data item 122 that the user 106 selected.

The database 120 stores data items 122, as described above. In at leastone example, the data items 122 can be associated with one or moregraphical elements that represent human bodies having different genders,heights, waist measurements, BMIs, etc. The data items 122, as describedabove, can be associated with one or more graphical elements 123A, 123B,etc. that represent human bodies. The one or more graphical elements123A, 123B, etc. associated with each data item 122 each represent ahuman body having a same set of magnitudes, as described above. The dataitems 122 illustrated in FIG. 1 are examples of data items 122 that canbe presented to users 106, and any other presentation or configurationcan be used.

The database 120 can be associated with an index, such as a lookuptable, where the data items 122 can be indexed based on magnitudesand/or other characteristics (e.g., gender, etc.). In a non-limitingexample, the data items 122 can be indexed by gender, height, dimension(e.g., waist measurement, BMI, etc.), etc. The index can expedite thetime required to retrieve data items 122 from the database 120 andreduce runtime computations associated with retrieving the data items122. As a result, the index enables the data collection module 116and/or presentation module 126 to retrieve data items 122 that arestored in the database 120 more efficiently (i.e., faster) then ifhumans were involved.

The measurement estimation module 124 accesses user data 118 and logsassociated with data associated with selections of data items 122. In atleast one example, as described above, the user data 118 and logs can bemapped to and/or otherwise associated with a profile corresponding to auser 106 that is stored in the database associated with the user data118. In such examples, the measurement estimation module 124 can accessthe profile stored in the database for estimating physical measurementsof a user 106. The measurement estimation module 124 utilizes the userdata 118 and magnitudes associated with the logs to estimate physicalmeasurements associated with the user 106.

The measurement estimation module 124 can utilize one or more predictivemodels to compute the estimated physical measurements. The one or morepredictive models can change based on the dimensions to be estimated,population of users, demographic of the population of users, etc.Predictive Models 1 and 2, reproduced below, are non-limiting examplesof predictive models that the measurement estimation module 124 can usefor computing the estimated physical measurements (e.g., BMI and waistmeasurement, respectively). In each of the predictive models below, theweights (β_(x)) are based at least in part on training the predictivemodel described below using user data 118, including previous inputs.Predictive Model 1 can be used to predict a user's 106 BMI (inkilograms/meters²) and Predictive Model 2 can be used to predict auser's 106 waist measurement (in millimeters). The “selected_waist” and“selected_bmi” variables are determined from a most recent (e.g., thelast) data item 122 selected by the user 106.

predicted_bmi=β₀+β₁ is_male+β₂ selected_bmi+β₃ selected_waist+β₄selected_waist:selected_bmi  PREDICTIVE MODEL 1

predicted_waist=β₀+β₁ is_male+β₂ selected_bmi+β₃ selected_waist+β₄selected_waist:selected_bmi  PREDICTIVE MODEL 2

In both examples, selected_waist:selected_bmi denotes the product of theselected_waist and selected_bmi.

The presentation module 126 can generate user interfaces that providevarious functionalities, as described above and also below. Thepresentation module 126 can cause a user interface to be presented tousers 106 utilizing any communication channel, such as an e-mailmessage, a site (e.g., website) associated with the service provider102, a text message, a social network site, an application that isassociated with the service provider 102 and that resides on device(s)108 of the users 106, etc. In at least one example, the presentationmodule 126 can generate a user interface that includes a set of dataitems (e.g., a subset of the data items 122 stored in the database 120).The user interface can be configured to receive input from the user 106and/or on behalf of the user 106 indicating a selection of at least onedata item 122 in the set of data items. The presentation module 126 cancause the user interface to be presented on a display 130 of a device108.

A non-limiting example of a user interface 132 is illustrated in FIG. 1.User interface 132 includes a set of data items 134, as shown by thedashed lines. The set of data items 134 includes individual data items122, as described above. In FIG. 1, the data items 122 are associatedwith two graphical elements 123A and 123B that each represent a humanbody having a set of magnitudes (i.e., pairs of graphical elements).

In additional and/or alternative examples, the presentation module 126can generate a user interface that prompts users 106 for userinformation such as gender, height, etc., and/or a user interface thatpresents estimated physical measurements to the users 106.

The training module 128 can train the predictive model based at least inpart on user data 118, including determined physical measurements (e.g.,physical measurements determined using a measuring device), dataassociated with user selections, and estimated physical measurements, asdescribed in FIG. 4 below. The predictive model can include a multipleregression model, etc., as described above in the non-limiting examplesof Predictive Model 1 and Predictive Model 2.

Example Processes

The processes described in FIGS. 2-4 below are illustrated as acollection of blocks in a logical flow graph, which represent a sequenceof operations that can be implemented in hardware, software, or acombination thereof. In the context of software, the blocks representcomputer-executable instructions stored on one or more computer-readablestorage media that, when executed by one or more processors, perform therecited operations. Generally, computer-executable instructions includeroutines, programs, objects, components, data structures, and the likethat perform particular functions or implement particular abstract datatypes. The order in which the operations are described is not intendedto be construed as a limitation, and any number of the described blockscan be combined in any order and/or in parallel to implement theprocesses.

FIG. 2 is a flow diagram that illustrates an example process 200 toestimate physical measurements utilizing data items 122 associated withone or more graphical elements 123A, 123B, etc. representing humanbodies that are presented to users 106.

Block 202 illustrates receiving user data 118. The data collectionmodule 116 can receive data (e.g., user data 118) associated with users106 from the users 106 and/or on behalf of the users 106 and/or accessdata associated with users 106 via third party sources and systems(e.g., social networks, professional networks, etc.), as describedabove. In at least one example, the presentation module 126 can generatea user interface that prompts a user 106 to input his or her gender andheight as illustrated in the user interface associated with block 202.Additional and/or alternative user interfaces can be presented forprompting the user 106 for additional and/or alternative information.The presentation module 126 can cause the user interface to be presentedto a user 106 via a display 130 of a device 108, as described above. Auser 106 can indicate whether he or she is a male or female,respectively, and select his or her height from one or more drop downmenus.

The user interface associated with block 202 is an example of a userinterface that can be presented to users 106 and any other presentationor configuration can be used. In FIG. 2, the user 106 has indicated thathe is a male and is 5′11″ tall. The data collection module 116 can senddata associated with the user's input (e.g., gender, height, etc.) tothe presentation module 126.

In some examples, process 200 can be executed without a user 106indicating whether he or she is male or female, respectively, orspecifying his or her height.

Block 204 illustrates causing a set of data items 134 to be presented toa user 106. In at least one example, the presentation module 126 canreceive user data 118 from the data collection module 116 and,leveraging the index associated with the database 120 described above,the presentation module 126 can retrieve data items 122 and generate auser interface that includes a set of data items 134 associated with theuser's input (e.g., gender, height, etc.). In an example, the set ofdata items 134 can represent a subset of data items 122 stored in thedatabase 120 that represent human bodies having a same set of magnitudeswith respect to every dimension except one dimension. That is, the setof data items 134 can include data items 122 that represent human bodieswith different magnitudes associated with one dimension, but otherwisehave the same set of magnitudes. In at least one example, based at leastin part on receiving the input indicating the gender and height of theuser, the presentation module 126 can access a predetermined number ofdata items 122 associated with the same gender and height as the user106. The predetermined number can be determined on a number of dataitems 122 that can be arranged on a user interface, an arbitrary number,etc.

Each data item 122 in a set of data items 134 can be associated with oneor more graphical elements that represent a human body with a differentmagnitude for a dimension than each of the other data items 122 in theset of data items 134. As a non-limiting example, each data item 122 ina set of data items 134 can be associated with one or more graphicalelements that represent a male who is 5′11″; however, each data item 122can be associated with one or more graphical elements that can representa 5′11″ male with a different waist measurement. In at least oneexample, each data item 122 can be associated with other magnitudes thatare associated with standardized measurements associated with apopulation, as described below. For instance, each data item 122 can beassociated with one or more graphical elements that represent a humanbody with a standardized BMI.

In additional and/or alternative examples, the set of data items 134 canrepresent a set of data items 134 stored in the database 120 thatrepresent human bodies having a different sets of magnitudes withrespect to different dimensions. For instance, in some examples, thepresentation module 126 can retrieve data items 122 and generate a userinterface that includes a set of data items 134 arranged in a matrixarrangement in which one axis is associated with BMI and the other axisis associated with waist measurement. That is, the data items 122represent human bodies with different BMIs along one axis and the dataitems 122 represent human bodies with different waist measurements alongthe other axis. In other examples, the presentation module 126 canretrieve data items 122 and generate a user interface that includes aset of data items 134 with varying dimensions that can be presented in apattern, a random configuration, etc. For instance, in at least oneexample, the set of data items 134 can include all of the data items 122stored in the database 120 and the presentation module 126 can presentall of the data items 122 to the user 106.

Block 206 illustrates receiving data associated with a user selection. Auser 106 can interact with a user interface to indicate which data item122 of the set of data items 134 is associated with at least onegraphical element representing a human body that looks most similar tohim or her, as described above. In at least some examples, users 106 canleverage zoom features to enlarge the individual data items 122. Thedevice 108 can determine an input associated with the user selection. Asa non-limiting example, box 206 indicates that the user selected thedata item 122 within the box 206 as the pair of graphical elements 123A,123B, etc. that are most representative of his body or of the body ofanother person. The device 108 can send data associated with the inputto the data collection module 116. The data collection module 116 canreceive the data and can analyze the input to determine the set ofmagnitudes associated with the data item 122 selected by the user 106.The data collection module 116 can send the data associated with the setof magnitudes to the measurement estimation module 124 and/or thepresentation module 126.

Block 208 illustrates estimating physical measurements. The measurementestimation module 124 accesses user data 118 and receives and/oraccesses the logs associated with the data indicating a selection of thedata item 122 that represents at least one graphical element of a humanbody that best represents the user's 106 own body. In additional and/oralternative examples, the measurement estimation module 124 can accessuser data 118 and receive and/or access the logs associated with thedata indicating a selection of the data item 122 that represents atleast one graphical element of a human body that best represents a bodyof another person (e.g., a friend, a family member, a suspectedcriminal/person of interest, etc.), as described above. The measurementestimation module 124 can utilize a predictive model (e.g., PredictiveModel 1, Predictive Model 2, etc.) to compute estimated physicalmeasurements based at least in part on the user data 118 (e.g., gender,height, etc.) and the magnitudes associated with user selections of dataitems 122. In at least one example, the measurement estimation module124 can estimate at least a waist measurement and/or BMI using apredictive model, like Predictive Models 1 and 2 described above.

Block 210 illustrates causing the physical measurements to be presentedto the user 106. The presentation module 126 can generate a userinterface that provides functionality to present estimated physicalmeasurements to the user 106 via the display 130 of the device 108. Thepresentation module 126 can send the user interface to the device 108for presenting the user interface to the user 106 on the display 130 ofthe device 108. An example user interface is associated with block 210.The user interface associated with block 210 is an example of a userinterface that can be presented to users 106 and any other presentationor configuration can be used.

The physical measurements can be presented to the user 106 as finitephysical measurements, ranges of physical measurements, physicalmeasurements including a confidence interval, etc. As a non-limitingexample, the physical measurements are presented as physicalmeasurements including confidence intervals. For instance, themeasurement estimation module 124 estimated the waist measurement of theuser 106 represented in FIG. 2 as 868.9 millimeters with a confidenceinterval of ±100 millimeters. That is, the user's 106 waist is likely tomeasure 768.9-968.9 millimeters. One or more graphical representationscan also be presented with the physical measurements to visuallyrepresent physical measurements associated with the user 106.

Block 212 illustrates iteratively causing sets of data items 134 to bepresented to the user 106 and iteratively receiving data associated withthe user selections. In at least some examples, based at least in parton receiving data associated with a first input, the presentation module126 can receive and/or access data from the data collection module 116that is associated with the magnitudes of the data item 122 selected bythe user 106 and can leverage the index associated with the database 120to retrieve a new set of data items 134. The new set of data items 134can include a same number of data items 122 as the first set of dataitems 134, more data items 122, or fewer data items 122. Thepresentation module 126 can generate a user interface that includes thenew set of data items 134 that are associated with one or more graphicalelements that represent human bodies with at least some new magnitudes.

Data items 122 in the new set of data items 134 can each be associatedwith one or more graphical elements that represent human bodies with atleast one magnitude associated with a dimension that is different fromthe other data items 122 in the new set of data items 134 but otherwisehave the same magnitudes as the other data items 122 in the new set ofdata items 134 with respect to other dimensions. In some examples, atleast one of the magnitudes associated with all of the data items 122 inthe new set of data items 134 is determined based at least in part on amagnitude associated with the data item 122 selected by the user 106 inthe first set of data items 134. The presentation module 126 can cause auser interface associated with the new set of data items 134 to bepresented on a display 130 of a device 108, as described above.

In at least one example, based at least in part on receiving dataassociated with the first input indicating which data item 122 in a setof data items 134 is associated with at least one graphical element thatrepresents a human body that best represents the user 106, the datacollection module 114 can analyze the selection and determine themagnitudes associated with the data item 122 selected by the user 106.The data collection module 114 can log each of the inputs and associatethe logs with the profile corresponding to the user 106 in the databaseassociated with the user data 118 and/or other data repository. Forinstance, if the user 106 selected a data item 122 associated with agraphical element that represents a human body with a waist measurementof 35 inches, the data collection module 114 can associate that log thecorresponds to the user selection with the user profile corresponding tothe user 106.

Based at least in part on determining the magnitudes associated with thedata item 122 selected by the user 106, the presentation module 126 canaccess a predetermined number of data items 122 that are associated withthe same gender, height, and magnitude (e.g., waist measurement of 35inches) to select a new set of data items 134. The new set of data items134 can include data items 122 that are associated with graphicalelements that represent human bodies that have different magnitudesassociated with a different dimension than the data items 122 in thefirst set of data items 134.

As a non-limiting example, if the data items 122 of the first set ofdata items 134 are associated with one or more graphical elements thatrepresent human bodies that each have a waist measurement, the dataitems 122 of the new set of data items 122 can be associated with one ormore graphical elements that represent human bodies that are associatedwith one or more graphical elements that represent human bodies thateach have a different BMI. As such, in the non-limiting example, the newset of data items 122 can be associated with graphical elements thatrepresent human bodies that have a same gender, same height, waistmeasurement, and varying BMIs. Each data item 122 can be associated withat least one graphical element that represents a human body with adifferent magnitude for the new dimension (e.g., BMI). As a non-limitingexample, every data item 122 in the new set of data items 134 can beassociated with graphical elements that represent a male who is 5′11″with a waist measurement of 35 inches; however, each data item 122 canbe associated with graphical elements that represent a 5′11″ male with awaist measurement of 35 inches and a different BMI.

In at least one example, a new set of data items 134 can include atleast one data item 122 that was in the first set of data items 134. Forinstance, as a non-limiting example, the first set of data items 134 canbe associated with data items 122 associated with one or more graphicalelements that represent human bodies that have a same gender, sameheight, same BMIs, and various waist measurements. The user can select adata item 122 associated with a graphical element representing a humanbody having a BMI of 26 and a waist measurement (e.g., 35 inches).Accordingly, the new set of data items 134 can be associated with dataitems 122 associated with one or more graphical elements representing ahuman body having a waist measurement of 35 inches and varying BMIs,including the BMI associated with the data items 122 in the first set ofdata items 134 (e.g., 26). Therefore, in at least one example, a dataitem 122 in an immediately preceding set of data items 134 can also beincluded in the new set of data items 134.

The presentation module 126 can iteratively define new sets of dataitems 134 to be presented to the user 106. As described above, each newset of data items 134 can be associated with data items 122 that areassociated with one or more graphical elements that represent humanbodies that have a different magnitude associated with a new dimension.The presentation module 126 can cause the sets of data items 134 to bepresented to the user 106 via a user interface configured to receiveinput. Based at least in part on causing the sets of data items 134 tobe presented to the user 106, the device 108 can determine inputs andsend data corresponding to the inputs to the data collection module 114.The data collection module 114 can receive inputs indicating which dataitem 122 is associated with at least one graphical element that bestrepresents the user 106. The measurement estimation module 124 canleverage the user data 118, including the data associated with theinputs, and the predictive model to estimate the physical measurementsassociated with the user 106, as described above. In some examples, themeasurement estimation module 124 can leverage the user data 118,including the data associated with the inputs, and the predictive modelto estimate the physical measurements associated with another person, asdescribed above.

FIG. 3 is a flow diagram that illustrates another example process 300 toestimate physical measurements utilizing data items 122 associated withgraphical elements that represent human bodies that are presented tousers 106.

Block 302 illustrates determining user data 118. The data collectionmodule 116 can receive data associated with users 106 from the users 106and/or on behalf of the user 106 and/or access data associated withusers 106 via third party sources and systems (e.g., social networks,professional networks, etc.), as described above. In at least oneexample, the presentation module 126 can generate a user interface thatprompts a user 106 for his or her gender, height measurement, and/orother dimensions, as described above.

Block 304 illustrates causing a first set of data items 122 to bepresented to the user 106. In at least one example, the presentationmodule 126 can access user data 118 and can utilize the index associatedwith the database 120 to retrieve data items 122 associated with one ormore graphical elements that represent human bodies with at least someof the same physical measurements as the user 106 (per the user data118). The presentation module 126 can utilize the retrieved data items122 to generate a user interface that includes a first set of data items134. The user interface can be configured to receive user selection ofone or more data items 122.

In at least one example, based at least in part on receiving the inputindicating the gender and height of the user 106, the presentationmodule 126 can retrieve a predetermined number of data items 122associated with graphical elements that represent human bodies that areassociated with the same gender and height. Each data item 122 in thefirst set of data items 134 can be associated with one or more graphicalelements that represent a human body with at least one differentmagnitude for a dimension and same magnitudes for each of the otherdimensions.

As a non-limiting example, each data item 122 in the first set of dataitems 134 associated with block 304 can be associated with one or moregraphical elements that represent a human body of the same gender andheight, but each data item 122 can have a different waist measurement.In at least one example, each data item 122 can be associated with oneor more graphical elements that represent human bodies that have a samepredetermined BMI. In at least one example, the predetermined BMI can bea standardized BMI (e.g., average, median, etc.) for a population ofusers who have been previously measured. That is, each data item 122 canbe associated with graphical elements that represent human bodies with agender and a height that are the same as the user 106, a samepredetermined BMI, and a different waist measurement.

Block 306 illustrates receiving data associated with a first input. Auser 106 can interact with a user interface to indicate which data item122 of the first set of data items 134 is associated with one or moregraphical elements that look most similar to him or her. As describedabove, in some examples, a user 106 can interact with a user interfaceto indicate which data item 122 of the second set of data items 134 isassociated with one or more graphical elements that look most similar toanother person (e.g., a friend, a family member, a suspectedcriminal/person of interest, etc.).

As a non-limiting example, box 306 indicates that the user 106 selectedthe data item 122 within the box 306 as the data item 122 associatedwith graphical elements most representative of his body. The device 108can determine the selection and can send data corresponding to the inputto the data collection module 116. The data collection module 116 canreceive the data associated with the first input and can determine themagnitudes associated with the data item 122 selected by the user 106 inthe first input. The data collection module 116 can log the first inputand associate the log with the user profile corresponding to the user106. The data collection module 116 can send data associated with thefirst input (e.g., the magnitudes associated with the data item 122selected by the user 106) to the presentation module 126.

Block 308 illustrates causing a second set of data items 134 to bepresented to the user 106. As described above, the presentation module128 can iteratively cause additional sets of data items 134 to bepresented to the user 106. In at least one example, based at least inpart on receiving the first input 306 indicating which data item 122 isassociated with one or more graphical elements that best represent theuser 106, the data collection module 114 can analyze the selection anddetermine the magnitudes associated with the data item 122 selected bythe user 106, as described above. The presentation module 126 canreceive data associated with the input 306 (e.g., the magnitudesassociated with the data item 122 selected by the user 106) and canutilize the data and the index associated with the database 120 toaccess a predetermined number of data items 122 that represent humanbodies having the same gender, height, and set of magnitudes, with theexception of magnitudes associated with one dimension. The second set ofdata items 134 can be associated with one or more graphical elementsthat represent human bodies that have different magnitudes associatedwith a different dimension than the first set of data items 134.

As a non-limiting example, if the data items 122 in the first set ofdata items 122 are associated with one or more graphical elements thatrepresent human bodies that have different waist measurements, the dataitems 122 in the second set of data items 134 can be associated with oneor more graphical elements that represent human bodies that have adifferent BMI. Each data item 122 can represent a human body with adifferent magnitude for the dimension. For instance, each data item 122can be associated with one or more graphical elements that represent ahuman body with a height and gender that is the same as the user 106, awaist measurement that corresponds to the waist measurement associatedwith the previously selected data item 122, and a different BMI. In someexamples, as described above, one of the data items 122 in the secondset of data items 134 can have a same BMI as the data item 122 selectedin the first set of data items 134.

Block 310 illustrates receiving data associated with a second input fromthe user 106. A user 106 can interact with a user interface to indicatewhich data item 122 of the second set of data items 134 is associatedwith one or more graphical elements that look most similar to him orher. As described above, in some examples, a user 106 can interact witha user interface to indicate which data item 122 of the second set ofdata items 134 is associated with one or more graphical elements thatlook most similar to another person (e.g., a friend, a family member, asuspected criminal/person of interest, etc.).

As a non-limiting example, the box 310 indicates that the user 106selects the data item within the box 310 as the data item associatedwith one or more graphical elements that are most representative of hisbody. The device 108 can determine the user selection and can send dataassociated with the second input to the data collection module 116. Thedata collection module 116 can receive the data associated with thesecond input and can determine the magnitude associated with the dataitem 122 selected by the user 106. The data collection module 116 canlog the second input and associate the log with the user 106 in the userdata 118. The data collection module 116 can send data associated withthe second input (e.g., the magnitudes associated with the data item 122selected by the user 106) to the presentation module 126.

Block 312 illustrates causing a third set of data items 134 to bepresented to the user 106. As described above, the presentation module128 can iteratively cause additional sets of data items 134 to bepresented to the user 106. In at least one example, based at least inpart on receiving data associated with the second input indicating dataitem 122 is associated with one or more graphical elements that bestrepresent the user 106, the data collection module 114 can analyze theselection and determine the magnitudes associated with the data item 122selected by the user 106, as described above. The presentation module126 can receive data associated with the second input (e.g., themagnitudes associated with the data item 122 selected by the user 106)and can utilize the data and the index associated with the database 120to access a predetermined number of data items 122 that are associatedwith one or more graphical elements that represent human bodies havingthe same gender, height, and set of magnitudes, with the exception ofmagnitudes associated with one dimension. The third set of data items134 can be associated with one or more graphical elements that representhuman bodies that have different magnitudes associated with a differentdimension than the first set and/or second set of data items 134.

As a non-limiting example, if the second set of data items 134 areassociated with one or more graphical elements that represent humanbodies that have a different BMI, the third set of data items 134 can beassociated with one or more graphical elements that represent humanbodies that have a different waist measurement and/or other magnitudeassociated with another dimension. Each data item 122 can be associatedwith one or more graphical elements that represent a human body with adifferent magnitude for the dimension. For instance, data item 122 canbe associated with one or more graphical elements that represent a humanbody with a height and gender that is the same as the user 106, a BMIthat corresponds to the BMI associated with the previously selectedgraphical element 122, and different waist measurements (or otherdimension).

Block 314 illustrates receiving data associated with a third input. Auser 106 can interact with a user interface to indicate which data item122 of the third set of data items 134 is associated with one or moregraphical elements that look most similar to him or her. As describedabove, in some examples, a user 106 can interact with a user interfaceto indicate which data item 122 of the second set of data items 134 isassociated with one or more graphical elements that look most similar toanother person (e.g., a friend, a family member, a suspectedcriminal/person of interest, etc.).

As a non-limiting example, the box 314 indicates that the user 106selects the data item 122 within the box 314 as the data item 126associated with one or more graphical elements that are mostrepresentative of his body. The device 108 can determine the userselection and can send data associated with the third input to the datacollection module 116. The data collection module 116 can receive thethird input and can determine the magnitudes associated with the dataitem 122 selected by the user 106. The data collection module 116 canlog the third input and associate the log with the user 106 in the userdata 118. The data collection module 116 can send data associated withthe third input (e.g., the magnitudes associated with the data item 122selected by the user 106) to the measurement estimation module 124.

Block 316 illustrates estimating physical measurements associated withthe user 106 or the other person, as described above. The measurementestimation module 124 accesses user data 118 including inputs indicatinga selection of at least one data item 122 associated with one or moregraphical elements that that best represents the user's 106 body. Inadditional and/or alternative examples, the measurement estimationmodule 124 can access user data 118 including inputs indicating aselection of at least one data item 122 associated with one or moregraphical elements that that best represents another person's body. Themeasurement estimation module 124 can utilize a predictive model (e.g.,Predictive Model 1, Predictive Model 2, etc.) to compute estimatedphysical measurements based at least in part on the user data 118 (e.g.,user gender, height, etc.) and the inputs. In at least one example, themeasurement estimation module 124 can estimate at least a waistmeasurement and/or BMI using a predictive model like the predictivemodels described above.

Process 300 includes three iterations of causing sets of data items 122to be presented to users 106 and receiving data associated with inputs,but any number of iterations can be used to estimate physicalmeasurements. In some examples, the data collection module 116 canreceive data associated with inputs following each presentation of a setof data items 134 and can subsequently send the data associated witheach input to the measurement estimation module 124. In other examples,after the last iteration, the data collection module 116 can receivedata associated with a last input (e.g., the magnitudes associated withthe last data item 122 selected by the user 106) and can subsequentlysend the data to the measurement estimation module 124 for estimatingthe physical measurements.

FIG. 4 is a flow diagram that illustrates an example process 400 totrain a predictive model.

Block 402 illustrates accessing user data 118 and data associated withinputs. The training module 128 can receive and/or access user data 118from the data collection module 116. In at least one example, thetraining module 128 can access determined physical measurementsassociated with users 106 (i.e., physical measurements ascertained bymeasurement devices). Additionally, the training module 128 can accessdata associated with inputs associated with users 106. As describedabove, the inputs can be associated with user selections of data items122 from each set of data items 134 caused to be presented to the user106. The determined physical measurements and the data associated withthe inputs can be associated with profiles corresponding to the users106 and stored in the database associated with the user data 118 and/orsome other data repository associated with the user data 118.

Block 404 illustrates accessing estimated physical measurements. Thetraining module 128 can access the estimated physical measurementsdetermined by the measurement estimation module 124. The estimatedphysical measurements can also be associated with each individual user106 and/or the determined physical measurements and data associated withthe inputs.

Block 406 illustrates training predictive models. The training module128 can leverage machine learning algorithms (e.g., supervised learning,unsupervised learning, semi-supervised learning, deep learning, etc.) tolearn predictive models. The measurement estimation module 124 canleverage the predictive models to estimate physical measurements basedat least in part on one or more determined physical measurements and theinputs. Predictive Model 1 and Predictive Model 2, described above, areexamples of predictive models learned using the machine learningalgorithms.

Example Clauses

A. A computer-implemented method for estimating physical measurementsassociated with users based at least in part on generating personalizeduser interfaces that provide functionality for receiving user input, thecomputer-implemented method comprising: accessing a database storing aplurality of data items; causing a set of data items of the plurality ofdata items to be presented to a user via a user interface that ispresented on a display of a device associated with the user, whereinfirst individual data items in the set of data items are associated withat least one graphical element representing a human body with individualmagnitudes corresponding to individual dimensions of a plurality ofdimensions; receiving first data from the device, the first dataindicating a selection of a first individual data item from the firstindividual data items, the first individual data item being associatedwith a first individual magnitude of the individual magnitudesassociated with a first dimension of the plurality of dimensions and asecond individual data item being associated with a second individualmagnitude of the individual magnitudes associated with a seconddimension of the plurality of dimensions; and estimating physicalmeasurements associated with the user based at least in part on at leastone of the first individual magnitude or the second individualmagnitude.

B. A computer-implemented method as paragraph A recites, furthercomprising: based at least in part on receiving the first data, causingan additional set of data items of the plurality of data items to bepresented to the user via the user interface, wherein second individualdata items in the additional set of data items are associated with atleast an additional graphical element representing the human body withthe second individual magnitude and third individual magnitudes of theindividual magnitudes associated with a third dimension of the pluralityof dimensions; receiving second data from the device, the second dataindicating an additional selection of a second individual data item fromthe second individual data items, the second individual data item beingassociated with a third individual magnitude of the third individualmagnitudes; and estimating the physical measurements associated with theuser further based at least in part on the third individual magnitude.

C. A computer-implemented method as paragraph B recites, wherein thethird dimension corresponds to the first dimension.

D. A computer-implemented method as paragraph C recites, wherein atleast one data item in the additional set of data items comprises thefirst individual data item.

E. A computer-implemented method as any of paragraphs A-D recite,wherein: the first dimension corresponds to a body mass indexmeasurement; the second dimension corresponds to a waist measurement;and estimating the physical measurements comprises estimating a bodymass index measurement of the user and a waist measurement of the user.

F. A computer-implemented method as any of paragraphs A-E recite,wherein individual data items of the plurality of data items stored inthe database are associated with graphical elements representing thehuman body that correspond to a same height and a same gender as theuser.

G. A computer-implemented method as any of paragraphs A-F recite,wherein the estimating the physical measurements associated with theuser comprises estimating the physical measurements further based atleast in part on a multiple regression predictive model.

H. A computer-implemented method as any of paragraphs A-G recite,wherein: individual data items of the plurality of data items in thedatabase are associated with two graphical elements representing thehuman body; and each graphical element of the two graphical elementscorresponds to a different view of the human body.

I. A computer-implemented method as any of paragraphs A-H recite,further comprising: generating an additional user interface thatprovides functionality to present the physical measurements to the uservia the display of the device; and sending the additional user interfaceto the device.

J. One or more computer-readable media encoded with instructions that,when executed by a processor, configure a computer to perform a methodas any of paragraphs A-I recite.

K. A device comprising one or more processors and one or more computerreadable media encoded with instructions that, when executed by the oneor more processors, configure a computer to perform acomputer-implemented method as any of paragraphs A-I recite.

L. A computer-implemented method for estimating physical measurementsassociated with users based at least in part on generating personalizeduser interfaces that provide functionality for receiving user input, thecomputer-implemented method comprising: means for accessing a databasestoring a plurality of data items; means for causing a set of data itemsof the plurality of data items to be presented to a user via a userinterface that is presented on a display of a device associated with theuser, wherein first individual data items in the set of data items areassociated with at least one graphical element representing a human bodywith individual magnitudes corresponding to individual dimensions of aplurality of dimensions; means for receiving first data from the device,the first data indicating a selection of a first individual data itemfrom the first individual data items, the first individual data itembeing associated with a first individual magnitude of the individualmagnitudes associated with a first dimension of the plurality ofdimensions and a second individual data item being associated with asecond individual magnitude of the individual magnitudes associated witha second dimension of the plurality of dimensions; and means forestimating physical measurements associated with the user based at leastin part on at least one of the first individual magnitude or the secondindividual magnitude.

M. A computer-implemented method as paragraph L recites, furthercomprising: based at least in part on receiving the first data, meansfor causing an additional set of data items of the plurality of dataitems to be presented to the user via the user interface, wherein secondindividual data items in the additional set of data items are associatedwith at least an additional graphical element representing the humanbody with the second individual magnitude and third individualmagnitudes of the individual magnitudes associated with a thirddimension of the plurality of dimensions; means for receiving seconddata from the device, the second data indicating an additional selectionof a second individual data item from the second individual data items,the second individual data item being associated with a third individualmagnitude of the third individual magnitudes; and means for estimatingthe physical measurements associated with the user further based atleast in part on the third individual magnitude.

N. A computer-implemented method as paragraph M recites, wherein thethird dimension corresponds to the first dimension.

O. A computer-implemented method as paragraph N recites, wherein atleast one data item in the additional set of data items comprises thefirst individual data item.

P. A computer-implemented method as any of paragraphs L-O recite,wherein: the first dimension corresponds to a body mass indexmeasurement; the second dimension corresponds to a waist measurement;and estimating the physical measurements comprises means for estimatinga body mass index measurement of the user and a waist measurement of theuser.

Q. A computer-implemented method as any of paragraphs L-P recite,wherein individual data items of the plurality of data items stored inthe database are associated with graphical elements representing thehuman body that correspond to a same height and a same gender as theuser.

R. A computer-implemented method as any of paragraphs L-Q recite,wherein the estimating the physical measurements associated with theuser comprises means for estimating the physical measurements furtherbased at least in part on a multiple regression predictive model.

S. A computer-implemented method as any of paragraphs L-R recite,wherein: individual data items of the plurality of data items in thedatabase are associated with two graphical elements representing thehuman body; and each graphical element of the two graphical elementscorresponds to a different view of the human body.

T. A computer-implemented method as any of paragraphs L-S recite,further comprising: means for generating an additional user interfacethat provides functionality to present the physical measurements to theuser via the display of the device; and means for sending the additionaluser interface to the device.

U. A system comprising: one or more processors; memory; and one or moremodules stored in the memory and executable by the one or moreprocessors to perform operations comprising: generating a user interfacefor presenting a first set of data items of a plurality of data items toa user, wherein first individual data items in the first set of dataitems represent human bodies having at least first magnitudes of a setof magnitudes associated with first dimensions and second magnitudes ofthe set of magnitudes associated with second dimensions; receiving dataindicating a selection of a first individual data item of the firstindividual data items; computing estimated physical measurementsassociated with the user based at least in part on the selection; andgenerating a second user interface for presenting the estimated physicalmeasurements to the user.

V. The system as paragraph U recites, wherein the operations furthercomprise, determining, based at least in part on the selection of thefirst individual data item, a first magnitude of the first magnitudesrepresented by the first individual data item and a second magnitude ofthe second magnitudes represented by the first individual data item.

W. The system as paragraph V recites, wherein: the operations furthercomprise based at least in part on receiving the data, generating atleast one additional user interface for presenting an additional set ofdata items of the plurality of data items to the user; second individualdata items in the additional set of data items are associated with thefirst magnitude or the second magnitude; and the second individual dataitems are each associated with a different magnitude associated with athird magnitude of the set of magnitudes associated with a thirddimension.

X. The system as paragraph W recites, wherein the operations furthercomprise: receiving additional data indicating an additional selectionof a second individual data item from the second individual data items;and computing the estimated physical measurements further based at leastin part on the additional selection.

Y. A first device comprising: one or more processors; memory; and one ormore modules stored in the memory and executable by the one or moreprocessors to perform operations comprising: causing a first userinterface of a first set of data items to be presented to a userassociated with the first device, wherein first individual data items inthe first set of data items represent human bodies having at least firstmagnitudes associated with a first dimension and second magnitudesassociated with a second dimension, wherein individual second magnitudesof the second magnitudes are different for each first individual dataitem of the first individual data items; determining a first inputindicating a first selection of a first individual data item of thefirst individual data items, wherein the first individual data item isassociated with an individual second magnitude of the individual secondmagnitudes; sending first data corresponding to the first input to asecond device; receiving second data from the second device, the seconddata corresponding to estimated physical measurements determined basedin part on the individual second magnitude; and causing a data itemcorresponding to the estimated physical measurements to be presented tothe user via the first device.

Z. A first device as paragraph Y recites, wherein the first magnitudesassociated with first dimensions correspond to a standardizedmeasurement associated with a population of previously measured users.

AA. A first device as any of paragraphs Y or Z recite, wherein theoperations further comprise: based at least in part on sending the firstdata, causing a second user interface of a second set of data items tobe presented to the user, wherein second individual data items in thesecond set of data items represent human bodies having the secondmagnitude and third magnitudes associated with a third dimension,wherein individual third magnitudes of the third magnitudes aredifferent for each second individual data item of the second individualdata items; determining a second input indicating a second selection ofa second individual data item of the second individual data items,wherein the second individual data item is associated with an individualthird magnitude of the individual third magnitudes; sending third datacorresponding to the second input to the second device; and receivingthe second data from the second device, the second data corresponding tothe estimated physical measurements further determined based in part onthe individual second magnitude and the individual third magnitude.

AB. A first device as paragraph AA recites, wherein the third dimensioncorresponds to the first dimension.

AC. A first device as paragraph AA recites, wherein the operationsfurther comprise: based at least in part on sending the third data,causing a third user interface of a third set of data items to bepresented to the user, wherein third individual data items in the thirdset of data items represent human bodies having the third magnitude andfourth magnitudes associated with a fourth dimension, wherein individualfourth magnitudes of the fourth magnitudes are different for each thirdindividual data item of the third individual data items; determining athird input indicating a third selection of a third individual data itemof the third individual data items, wherein the third individual dataitem is associated with an individual fourth magnitude of the individualfourth magnitudes; sending fourth data corresponding to the third inputto the second device; and receiving the second data from the seconddevice, the second data corresponding to the estimated physicalmeasurements further determined based in part on the individual secondmagnitude, the individual third magnitude, and the individual fourthmagnitude.

AD. A first device as paragraph AC recites, wherein the fourth dimensioncorresponds to the second dimension.

AE. A first device as paragraph AC recites, wherein: the first dimensionand the third dimension correspond to a body mass index measurement; thesecond dimension and the fourth dimension correspond to a waistmeasurement; and the estimated physical measurements include anestimated body mass index measurement and an estimated waistmeasurement.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are described as illustrative forms ofimplementing the claims.

Conditional language such as, among others, “can,” “could,” “might” or“can,” unless specifically stated otherwise, are understood within thecontext to present that certain examples include, while other examplesdo not necessarily include, certain features, elements and/or steps.Thus, such conditional language is not generally intended to imply thatcertain features, elements and/or steps are in any way required for oneor more examples or that one or more examples necessarily include logicfor deciding, with or without input or prompting, whether certainfeatures, elements and/or steps are included or are to be performed inany particular example. Conjunctive language such as the phrase “atleast one of X, Y or Z,” unless specifically stated otherwise, is to beunderstood to present that an item, term, etc. can be either X, Y, or Z,or a combination thereof.

1. A computer-implemented method comprising: accessing a databasestoring a plurality of data items; causing a set of data items of theplurality of data items to be presented to a user via a user interfacethat is presented on a display of a device associated with the user,wherein first individual data items in the set of data items areassociated with at least one graphical element representing a human bodywith individual magnitudes corresponding to individual dimensions of aplurality of dimensions; receiving first data from the device, the firstdata indicating a selection of a first individual data item from thefirst individual data items, the first individual data item beingassociated with a first individual magnitude of the individualmagnitudes associated with a first dimension of the plurality ofdimensions and a second individual data item being associated with asecond individual magnitude of the individual magnitudes associated witha second dimension of the plurality of dimensions; and estimatingphysical measurements associated with the user based at least in part onat least one of the first individual magnitude or the second individualmagnitude. 2-20. (canceled)